Augmediated reality system based on 3D camera selfgesture sensing
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Three-Dimensional (3D) range cameras have recently appeared in the marketplace for use in surveillance (e.g. cameras affixed to inanimate objects) applications. We present FreeGlass™ as a wearable hands-free 3D gesture-sensing Digital Eye Glass system. FreeGlass comprises a head-mounted display with an infrared range camera, both connected to a wearable computer. It is based on the MannGlas™ computerized welding glass, which embodies HDR (High Dynamic Range) and AR (Augmented/Augmediated Reality). FreeGlass recontextualizes the 3D range camera as a sousveillance (e.g. cameras attached to people) camera. In this sousveillance context, the range camera is worn by the user and shares the same point-of-view as the user. Computer vision algorithms therefore benefit from the use of the range camera to allow image segmentation by using both the infrared and depth information from the device for 3D hand gesture recognition system. The gesture recognition is then accomplished by using a neural network on the segmented hand. Recognized gestures are used to provide the user with interactions in an augmediated reality environment. Additionally, we present applications of FreeGlass for serendipitous gesture recognition in everyday life, as well as for interaction with real-world objects (with and without gesture recognition). A plurality of FreeGlass units can be used together, each sensor having a different spreading sequence, or the like, so that a number of people can collaborate and share the same or similar Augmediated Reality space(s).
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it